Complement data for: A mosquito brain like a sieve: evaluating the memory of mosquito larvae
Martin Dessart
26/09/2023
0) Setup - Data presentation
## ID sec frame pos_y
## 10 : 672 Min. : 52 Min. : 520 Min. : 3.561
## 1 : 671 1st Qu.: 748 1st Qu.: 7480 1st Qu.:136.735
## 4 : 670 Median : 1649 Median : 16490 Median :286.687
## 3 : 669 Mean : 2402 Mean : 24015 Mean :255.649
## 2 : 668 3rd Qu.: 2774 3rd Qu.: 27740 3rd Qu.:387.786
## 5 : 668 Max. :13718 Max. :137180 Max. :408.962
## (Other):12661
## pos_x dY absdY cat
## Min. : 19.89 Min. :-18.97029 Min. : 0.01638 11 :1576
## 1st Qu.: 310.80 1st Qu.: -4.71295 1st Qu.: 4.58448 2 :1544
## Median : 590.39 Median : -0.95374 Median : 15.05389 5 :1539
## Mean : 616.46 Mean : -2.33537 Mean : 26.30399 6 :1537
## 3rd Qu.: 928.99 3rd Qu.: 0.05349 3rd Qu.: 43.27109 9 :1530
## Max. :1227.18 Max. : 10.80695 Max. :169.44433 10 :1528
## (Other):7425
## gp ret ct pck expe
## Length:16679 1:2412 Min. :1.000 1:8919 5 : 879
## Class :character 2:2479 1st Qu.:3.000 2:5358 15 : 879
## Mode :character 3:2355 Median :4.000 3:2402 1 : 875
## 4:2333 Mean :3.431 7 : 869
## 5:2435 3rd Qu.:4.000 11 : 858
## 6:4665 Max. :4.000 16 : 840
## (Other):11479
## iti plong
## 10 min: 1630 Min. : 12.00
## 2 min :13572 1st Qu.: 29.00
## 5 min : 1477 Median : 39.00
## Mean : 46.28
## 3rd Qu.: 48.00
## Max. :219.00
## 1) Figure 2 GAM
GAM
2) Figure 3 RETENTION
Plot
## Stats
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: dY
## Chisq Df Pr(>Chisq)
## cat 20.329 2 3.852e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## $emmeans
## cat emmean SE df lower.CL upper.CL
## 1 31.6 2.58 65.0 26.5 36.8
## 10 18.9 2.48 64.2 14.0 23.9
## Test 18.9 2.64 65.5 13.6 24.1
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## 1 - 10 12.6740 3.21 44.9 3.943 0.0008
## 1 - Test 12.7391 3.33 45.6 3.823 0.0011
## 10 - Test 0.0651 3.26 45.5 0.020 0.9998
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: dY
## Chisq Df Pr(>Chisq)
## cat 12.705 2 0.001742 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## $emmeans
## cat emmean SE df lower.CL upper.CL
## 1 27.1 2.51 64 22.12 32.1
## 10 17.9 2.51 64 12.92 22.9
## Test 14.9 2.63 64 9.66 20.2
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## 1 - 10 9.20 3.55 45.9 2.590 0.0336
## 1 - Test 12.21 3.64 47.9 3.354 0.0044
## 10 - Test 3.01 3.64 47.9 0.828 0.6876
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: dY
## Chisq Df Pr(>Chisq)
## cat 9.387 2 0.009155 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## $emmeans
## cat emmean SE df lower.CL upper.CL
## 1 31.4 2.83 54.2 25.7 37.0
## 10 21.5 2.97 54.7 15.5 27.4
## Test 21.7 3.05 54.8 15.6 27.8
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## 1 - 10 9.888 3.77 37.3 2.626 0.0326
## 1 - Test 9.633 3.81 36.5 2.529 0.0411
## 10 - Test -0.255 3.97 40.1 -0.064 0.9977
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: dY
## Chisq Df Pr(>Chisq)
## cat 10.865 2 0.004371 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## $emmeans
## cat emmean SE df lower.CL upper.CL
## 1 33 2.70 65 27.6 38.4
## 10 22 3.05 65 15.9 28.1
## Test 22 2.64 65 16.7 27.3
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## 1 - 10 11.0255 4.08 49.2 2.704 0.0250
## 1 - Test 10.9809 3.78 45.1 2.906 0.0153
## 10 - Test -0.0445 4.03 46.6 -0.011 0.9999
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: dY
## Chisq Df Pr(>Chisq)
## cat 7.867 2 0.01958 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## $emmeans
## cat emmean SE df lower.CL upper.CL
## 1 32.3 2.43 64.8 27.5 37.2
## 10 23.6 2.67 68.0 18.3 28.9
## Test 30.4 2.57 66.7 25.3 35.5
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## 1 - 10 8.72 3.21 47.3 2.713 0.0247
## 1 - Test 1.94 3.10 44.8 0.624 0.8077
## 10 - Test -6.78 3.34 49.3 -2.033 0.1149
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: dY
## Chisq Df Pr(>Chisq)
## cat 8.9877 2 0.01118 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## $emmeans
## cat emmean SE df lower.CL upper.CL
## 1 34.8 3.36 42 28.1 41.6
## 10 19.9 3.76 42 12.3 27.5
## Test 27.6 3.36 42 20.8 34.3
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## 1 - 10 14.92 5.04 30.5 2.957 0.0159
## 1 - Test 7.29 4.76 29.3 1.533 0.2903
## 10 - Test -7.62 5.04 30.5 -1.511 0.2999
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
3) Figure 4 ITI VARIATION
## [1] "10 min" "2 min" "5 min"
STATS
STTTR1<-STTT %>% filter(iti=="2 min")
mR1<-lmerTest::lmer(dY~cat+(1|ID),data=STTTR1)## boundary (singular) fit: see ?isSingularsimR1 <- simulateResiduals(fittedModel = mR1, plot = T)
emmeans(mR1, pairwise ~ cat,adjust="tukey")## $emmeans
## cat emmean SE df lower.CL upper.CL
## 1 34.8 3.36 42 28.1 41.6
## 10 19.9 3.76 42 12.3 27.5
## Test 27.6 3.36 42 20.8 34.3
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## 1 - 10 14.92 5.04 30.5 2.957 0.0159
## 1 - Test 7.29 4.76 29.3 1.533 0.2903
## 10 - Test -7.62 5.04 30.5 -1.511 0.2999
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimatesSTTTR2<-STTT %>% filter(iti=="5 min")
mR2<-lmerTest::lmer(dY~cat+(1|ID),data=STTTR2)## boundary (singular) fit: see ?isSingularsimR2 <- simulateResiduals(fittedModel = mR2, plot = T)
emmeans(mR2, pairwise ~ cat,adjust="tukey")## $emmeans
## cat emmean SE df lower.CL upper.CL
## 1 29.7 3.93 40 21.8 37.7
## 10 23.5 4.11 40 15.2 31.8
## Test 32.8 3.29 40 26.1 39.4
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## 1 - 10 6.21 5.71 32.5 1.088 0.5279
## 1 - Test -3.06 5.13 27.8 -0.596 0.8236
## 10 - Test -9.27 5.27 28.7 -1.759 0.2012
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimatesSTTTR3<-STTT %>% filter(iti=="10 min")
mR3<-lmerTest::lmer(dY~cat+(1|ID),data=STTTR3)
simR3 <- simulateResiduals(fittedModel = mR3, plot = T)
emmeans(mR3, pairwise ~ cat,adjust="tukey")## $emmeans
## cat emmean SE df lower.CL upper.CL
## 1 29.9 3.37 42.7 23.12 36.7
## 10 15.4 3.90 43.0 7.57 23.3
## Test 29.3 3.49 42.8 22.24 36.3
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## 1 - 10 14.50 5.00 31.2 2.902 0.0180
## 1 - Test 0.66 4.66 27.5 0.142 0.9890
## 10 - Test -13.84 5.08 32.1 -2.723 0.0273
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates4) Supp Figure S1 T1 T2
## [1] "10 min" "2 min" "5 min"
Stats
mel1<-ST2 %>% filter(iti=="2 min")
mR1<-lmerTest::lmer(dY~cat+(1|ID),data=mel1)
simR1 <- simulateResiduals(fittedModel = mR1, plot = T)
emmeans(mR1, pairwise ~ cat,adjust="tukey")## $emmeans
## cat emmean SE df lower.CL upper.CL
## 1 34.8 3.06 26 28.5 41.1
## 2 21.0 3.61 26 13.6 28.4
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## 1 - 2 13.8 4.66 15.3 2.955 0.0096
##
## Degrees-of-freedom method: kenward-rogermel2<-ST2 %>% filter(iti=="5 min")
mR2<-lmerTest::lmer(dY~cat+(1|ID),data=mel2)## boundary (singular) fit: see ?isSingularsimR2 <- simulateResiduals(fittedModel = mR2, plot = T)
emmeans(mR2, pairwise ~ cat,adjust="tukey")## $emmeans
## cat emmean SE df lower.CL upper.CL
## 1 29.7 3.89 22 21.6 37.8
## 2 24.9 4.29 22 16.0 33.8
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## 1 - 2 4.84 5.79 13 0.836 0.4181
##
## Degrees-of-freedom method: kenward-rogermel3<-ST2 %>% filter(iti=="10 min")
mR3<-lmerTest::lmer(dY~cat+(1|ID),data=mel3)
simR3 <- simulateResiduals(fittedModel = mR3, plot = T)
emmeans(mR3, pairwise ~ cat,adjust="tukey")## $emmeans
## cat emmean SE df lower.CL upper.CL
## 1 30.1 2.87 29 24.2 36.0
## 2 30.4 3.20 29 23.8 36.9
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## 1 - 2 -0.291 4.21 16 -0.069 0.9457
##
## Degrees-of-freedom method: kenward-roger